Machine learning-based time-to-event survival analysis in pediatric patients with severe sepsis.

ABSTRACT:

Background

Pediatric sepsis remains a leading cause of mortality in critically ill children worldwide. Current approaches to sepsis prognosis rely on clinical criteria and biomarkers with variable performance. This study aimed to develop and validate time-to-event survival prediction models for pediatric sepsis using survival analysis machine learning algorithms.

Methods

We conducted a retrospective cohort study of 223 pediatric sepsis patients from a pediatric intensive care database (2010-2018). Five survival analysis machine learning algorithms were evaluated: CoxPHSurvivalAnalysis, HingeLossSurvivalSVM, GradientBoostingSurvivalAnalysis, RandomSurvivalForest, and ExtraSurvivalTrees. These algorithms predict survival time rather than binary outcomes. Model performance was

SUBMITTER: Huang Q 

PROVIDER: S-EPMC12589007 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature